Pap Smear Image Segmentation Using Chan-Vese-Based Adaptive Primal Dual Splitting Algorithm
B. Chitra, S. S. Kumar · Apple Academic Press eBooks · 2023
Cervical cancer is a deadly disease and it must be identified at an early stage for survival. The computer-aided screening process provides an early detection and analysis of cervical cancer is the precise cell segmentation. An automatic and accurate cervical pap smear image segmentation is considered an evergreen research problem. To address such shortcomings, this paper comprises three main phases, namely, the preprocessing phase, segmentation phase, as well as morphological operation. The input cervical cancer datasets are preprocessed in the initial phase. In phase 2, an appropriate segmentation is conducted by constructing a Chan Vese based adaptive primal-dual splitting algorithm to obtain an optimal segmented image set. In the third step, the morphological operation is carried out to obtain the segmentation results more accurately. The method used in this paper is computed by employing the dataset called the Herlev Pap smear dataset. The performance analysis and the comparative analysis are conducted and the results reveal that the proposed approach provides a high accuracy rate and efficiency when compared with other methods.